3 Must-Haves for Insurance (and General) Data Quality
Mo Data stashed this in Big Data in Insurance
The increasing number of channels is hurting data quality within insurance organizations, 94% suspect their customer and prospect data might be inaccurate in some way.
1. Identify data entry points — Insurers need to understand how information enters their system and through what means. Consider all channels and data entry points so a full data workflow can be created. Then prioritize projects based on high volume channels or excessive data quality errors.
2. Utilize automated verification processes — Software solutions can be implemented in various channels to prevent inaccurate information, like poor address and email contact details, from entering the database. Incorporating software solutions is the only way to ensure information self-entered by untrained users is accurate. Figure out what data is most important to the business and evaluate and prioritize available solutions.
3. Incorporate technology that continues to clean information over time — insurers should regularly monitor their databases. Even with software tools at the point-of-capture, regular database maintenance is required. Regular cleansing allows insurers to review information and make sure installed tools are still effective in managing data to the expected level of quality.
Stashed in: Big Data Preparation